Evidence map›Paper›PMID 34223417›Full record

ArticleJSES international2021

Using machine learning methods to predict nonhome discharge after elective total shoulder arthroplasty.

Cesar D Lopez, Michael Constant, Matthew J J Anderson, Jamie E Confino, John T Heffernan, Charles M Jobin

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PubMed Full text at PMC Publisher
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Article in JSES international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Cesar D LopezNew York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA.
Michael ConstantNew York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA.
Matthew J J AndersonNew York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA.
Jamie E ConfinoNew York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA.
John T HeffernanNew York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA.
Charles M JobinNew York-Presbyterian/Columbia University Irving Medical Center, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMachine learning has shown potential in accurately predicting outcomes after orthopedic surgery, thereby allowing for improved patient selection, risk stratification, and preoperative planning. This study sought to develop machine learning models to predict nonhome discharge after total shoulder arthroplasty (TSA).

methodsThe American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent elective TSA from 2012 to 2018. Boosted decision tree and artificial neural networks (ANN) machine learning models were developed to predict non-home discharge and 30-day postoperative complications. Model performance was measured using the area under the receiver operating characteristic curve (AUC) and overall accuracy (%). Multivariate binary logistic regression analyses were used to identify variables that were significantly associated with the predicted outcomes.

resultsThere were 21,544 elective TSA cases identified in the National Surgical Quality Improvement Program registry from 2012 to 2018 that met inclusion criteria. Multivariate logistic regression identified several variables associated with increased risk of nonhome discharge including female sex (odds ratio [OR] = 2.83; 95% confidence interval [CI] = 2.53-3.17;

conclusionsBoth the boosted decision tree and ANN models performed well in predicting nonhome discharge with similar overall accuracy, but the ANN had higher discriminative ability. Based on the findings of this study, machine learning has the potential to accurately predict nonhome discharge after elective TSA. Surgeons can use such tools to guide patient expectations and to improve preoperative discharge planning, with the ultimate goal of decreasing hospital length of stay and improving cost-efficiency.

Indexed as

ACS-NSQIPArtificial intelligenceArtificial neural networkDeep learningDischarge dispositionMachine learningNonhome dischargeTotal shoulder arthroplasty

Identifiers

PMID34223417
PMCPMC8245980

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.